The images of 2-D gels resulting from electrophoresis are a powerful biomedical diagnosis mechanism. The proteins of tissue, blood, cell, etc, are separated and analyzed by placing the proteins on a polyacrylamide gel and applying an electrical potential difference across it. The separation follows a bidimensional pattern according to the molecular weight and isoelectrical point of the proteins.
When analyzing gel images, a reference image that represents the distribution of a sample of proteins in normal conditions (normal health status) is utilized. The protein spots shown in reference images are labeled and have a known spatial location. Diagnostic test images are then generated, in which the identification and spatial location of the protein spots is unknown. Usually, a comparison between a test image and the reference image is performed in order to establish the correspondence between protein spots in both images. For each pair of corresponding protein spots, one protein spot in each image represents the same protein. For each pair of corresponding protein spots, the difference between the characteristics of each of protein spot infers information about changes to a particular protein. Extracting this information for a relevant subset of proteins can be used to diagnose a medical condition or to test for the presence of a drug, etc.
Although gels images are increasingly used in the biomedical field, the analysis of such images is becoming more difficult due to the variability between different electrophoresis processes. Consequently, test images with a complex correspondence to reference images may be obtained. The location, shape, size, and intensity of any given protein spot may vary between images or the protein may not appear in one of the images, such that correspondence between proteins in the test and reference images is difficult or impossible to establish. Since each gel may contain thousands of proteins, computational techniques are essential for efficient gel image analysis.